Value of a comprehensive geriatric assessment for predicting one-year outcomes in patients undergoing transcatheter aortic valve implantation: results from the CGA-TAVI multicentre registry.
Bibliographic record
Abstract
BACKGROUND: In a three-month report from the CGA-TAVI registry, we found the Multidimensional Prognostic Index (MPI) and Short Physical Performance Battery (SPPB) to be of value for predicting short-term outcomes in elderly patients undergoing transcatheter aortic valve implantation (TAVI). In the present analysis, we examined the association of these tools with outcomes up to one year post-TAVI. METHODS: CGA-TAVI is an international, observational registry of geriatric patients undergoing TAVI. Patients were assessed using the MPI and SPPB. Efficacy of baseline values and any postoperative change for predicting outcome were established using logistic regression. Kaplan-Meier analysis was carried out for each comprehensive geriatric assessment tool, with survival stratified by risk category. RESULTS: = 0.018). No significant associations were found between a high-risk SPPB score and mortality or two main combined endpoints. In contrast to a worsening SPPB, an aggravating MPI score at three months post-TAVI was associated with an increased risk of death and/or non-fatal stoke at one year (aOR = 95.16, 95% CI: 3.41-2657.01). CONCLUSIONS: The MPI showed value for predicting the likelihood of death and a combination of death and/or non-fatal stroke by one year after TAVI in elderly patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".